Vegetation map for Magela Creek floodplain using WorldView - 2 multispectral image data
T Whiteside & R Bartolo
The significance of the wetlands of the Magela Creek floodplain in northern Australia and their biodiversity has been recognised through their listing by the Ramsar Convention on Wetlands. The wetlands have been identified as being at risk from a number of sources, chiefly the landscape-scale risks of weeds, fire and climate change. In addition, the Magela Creek floodplain is a downstream receiving environment for the Ranger uranium mine. Offsite monitoring of this area will become increasingly important in the years following closure and rehabilitation of the minesite (post 2026), as a key component of an integrated environmental monitoring framework (particularly relevant to Key Knowledge Need 2.6.2 pertaining to off-site monitoring).
Vegetation within the wetland is spatially and temporally variable and, therefore, a robust methodology for mapping wetland vegetation at scales that can detect the variability is required. In addition, time series mapping of floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics on the floodplain to assist with analysing change during and after minesite rehabilitation.
The aim of this project is to:
To establish a baseline dataset of natural variability in vegetation that could be used to monitor potential mine impacts through the production of a series of high resolution vegetation maps 2010–2014.
Specifically, the objectives of this report are to:
- Document the procedures for producing a map of the floodplain vegetation for 2010 in a large wetland downstream from Ranger Uranium Mine.
- Determine the applicability of high spatial resolution (HSR) satellite imagery to map and monitor vegetation on the floodplain.
- Develop and record a GEOBIA-based methodology suitable for mapping and monitoring the offsite environment.
HSR satellite imagery consists of pixels with a ground sample distance (GSD) less than 5 m. HSR imagery, such as WorldView-2 provides data for spatially detailed analysis of landscapes. The increased spatial heterogeneity associated with the finer resolution of the data requires data aggregation to assist with classification. The research described here uses geographic object-based image analysis (GEOBIA) to classify the floodplain vegetation from 2010 WorldView-2 imagery. The GEOBIA used consisted of a step-wise rule-set driven approach using a series of segmentations and classifications. The rule-set implemented a number of well-known spectral indices and sensor band specific ratios to: (1) create and classify objects representing the major landscape units (floodplain and non-floodplain) and mask non-target land covers, and (2) extract objects representative of the vegetation communities within the floodplain. The input of a digital elevation model enabled the delineation of the floodplain boundary.
The main output of this project is a map of the major 12 vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. Based on the reference data the overall accuracy of the map is 78%. The rule set was able to distinguish the majority of the floodplain classes. Most of the error appears to be associated with confusion between classes that are spectrally similar such as the classes dominated by grasses. The other main output from this project is the development of a robust methodology for mapping the vegetation on the Magela Creek floodplain for 2010 and subsequent years using WorldView-2 imagery.
The major findings of this project are that WorldView-2 multispectral imagery is an appropriate data set for a vegetation classification of the Magela Creek floodplain. The application of a GEOBIA methodology is a suitable method for the increase within field spectral variation associated with HSR data.
The use of WV-2 high spatial resolution imagery enables boundary delineation between classes and also aids in the identification of individual or small clusters of plants. The red edge band within the imagery was useful for discrimination of a number of the classes. Limitations of the WV-2 data include the narrow spectral range (350–940 nm) and different view angle for the different images. The advantages of using a GEOBIA method included the ability to compile a rule set that is repeatable and potentially transferrable to other data, although it is limiting in lengthy processing time. The inclusion of a Canopy Height Model (CHM) was beneficial in enabling the accurate identification and mapping of treed areas within the floodplain although the coverage was not entire and is a different date to the WV-2 data.
The methodology described in this report will be applied to the WV-2 imagery acquired annually by eriss for 2011–2014. Vegetation maps from each year will be used in analysis of the spatial and temporal variability of the communities. This analysis will inform the temporal frequency of image acquisitions over the region as part of an ongoing monitoring program during and post mine site rehabilitation.